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All Journal Dinamik Progresif: Jurnal Ilmiah Komputer
Icha Winadya Permadani
Universitas Teknokrat Indonesia
Computer Science & IT Control & Systems Engineering

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Prediksi Risiko Kredit Nasabah Menggunakan Algoritma Data Mining: Studi Kasus pada PT Toyota Astra Finance Icha Winadya Permadani; Raka Sulistyo; Muhammad Fadli; Erliyan Redy Susanto
Progresif: Jurnal Ilmiah Komputer Vol 21, No 2 (2025): Agustus
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v21i2.2909

Abstract

This study aims to develop a credit risk prediction model for customers at PT Toyota Astra Financial Services using data mining algorithms, specifically Random Forest and XGBoost. In response to the challenge of non-performing loans (NPL), machine learning-based predictive models offer an effective solution to identify potential risks early. The research utilizes historical customer data encompassing demographic information, employment status, and loan history. After data preprocessing, the models were evaluated using accuracy, precision, recall, F1-score, and ROC-AUC metrics. The results indicate that XGBoost outperformed other models with an accuracy of 91.67% and an F1-score of 0.89 for the positive class. These findings demonstrate that applying machine learning algorithms can significantly enhance credit selection efficiency and reduce potential losses from defaulted loans.Keywords: Credit Risk; Machine learning; Random Forest; XGBoost, Data mining. AbstrakPenelitian ini bertujuan untuk membangun model prediksi risiko kredit nasabah pada PT Toyota Astra Financial Services dengan memanfaatkan algoritma data mining, khususnya Random Forest dan XGBoost. Dalam menghadapi tantangan kredit macet, model prediktif berbasis machine learning dapat memberikan solusi yang efektif untuk mengidentifikasi potensi risiko sejak dini. Penelitian ini menggunakan data historis nasabah yang mencakup informasi demografi, status pekerjaan, dan riwayat pinjaman. Setelah melalui tahap pra-pemrosesan data, model dievaluasi menggunakan metrik akurasi, presisi, recall, F1-score, dan ROC-AUC. Hasil menunjukkan bahwa XGBoost memiliki performa terbaik dengan akurasi sebesar 91,67% dan F1-score 0,89 pada kelas positif. Temuan ini menunjukkan bahwa penerapan algoritma machine learning dapat meningkatkan efisiensi seleksi kredit dan mengurangi potensi kerugian akibat kredit bermasalah.Kata kunci: Risiko Kredit; Machine learning; Random Forest; XGBoost; Data mining
Klasifikasi Tipe Pokémon Berdasarkan Statistik Tempur Menggunakan Algoritma Random Forest Nugroho Kumala Destianto; Yohanes Simarmata; Nurul Hidayanah; Icha Winadya Permadani; Heni Sulistiani
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10395

Abstract

This study aims to classify Pokémon types based on battle statistics using the Random Forest algorithm. The dataset used comes from the file pokemon_bw.csv, which contains information such as Pokédex number, name, type, abilities, and battle stat values (HP, Attack, Defense, Special Attack, Special Defense, Speed). Data preprocessing was carried out to clean and prepare the data, including primary type extraction, label encoding, feature selection, and feature standardization. After that, the dataset was split into training and testing data with an 80:20 ratio. The classification model was built using Random Forest with 100 decision trees and evaluated using accuracy, classification report, confusion matrix, and multiclass ROC Curve metrics. The results show that the model achieved an accuracy of 64.8%, with the best performance in the 'rock', 'steel', and 'dragon' classes, while the 'flying' and 'ghost' classes were still difficult to classify accurately. Confusion matrix. It shows that some types have quite significant misclassification errors, such as 'ground' which is often predicted as 'grass' and 'rock' which is often misclassified as 'steel'. ROC Curve evaluation also proves that most classes have an AUC above 0.80, indicating the model's ability to distinguish between classes. With this approach, this study provides an initial analysis of the potential for predicting Pokémon types based on battle statistics, which can be further developed through handling class imbalances or using other ensemble techniques.
Co-Authors Erliyan Redy Susanto Fadli, Muhammad Heni Sulistiani Nugroho Kumala Destianto Nurul Hidayanah Raka Sulistyo Yohanes Simarmata